FACE RECOGNITION BY: TEAM 1 • BILL BAKER • NADINE BROWN • RICK HENNINGS • SHOBHANA MISRA • SAURABH PETHE
FACE RECOGNITION • BIOMETRICS • EVOLVING APPROACHES TO RECOGNIZING FACES: • EIGENFACE TECHNOLOGY • LOCAL FEATURE ANALYSIS • NEURAL NETWORK TECHNOLOGY • ADVANTAGES/DISADVANTAGES • FUTURE
BIOMETRICS • Biometrics - digital analysis using cameras or scanners of biological characteristics such as facial structure, fingerprints and iris patterns to match profiles to databases of people
WHY DO WE NEED IT ? • Quick way to discover criminals • Criminals can easily change their appearance • Fake Id’s • Risks are higher than ever: • 9/11 • Anthrax • Etc. • Old ways are outdated
EIGENFACE TECHNOLOGY • BIOMETRIC SYSYEMS IN DEVELOPMENT FOR OVER 20 YEARS • FACE IMAGE CAPTURED VIA CAMERA AND PROCESSED USING AN ALGORITHM BASED ON PRINCIPLE COMPONENT ANALYSIS (PCA) WHICH TRANSLATES CHARACTERISTICS OF A FACE INTO A UNIQUIE SET OF NUMBERS (TEMPLATE) • FACE PRESENTED IN A FRONTAL VIEW WITH WIDE EXPRESSION CHANGE
EIGENFACE TECHNOLOGY • A set of Eigenfaces - two-dimensional face-like arrangements of light and dark areas, as shown to the right, is made by combining all the pictures and looking at what is common to groups of individuals and where they differ most
EIGENFACE TECHNOLOGY • To identify a face, the program compares its Eigenface characteristics, which are encoded into numbers called a template, with those in the database, selecting the faces whose templates match the target most closely, as shown to the right
LOCAL FEATURE ANALYSIS • Local feature analysis considers individual features. These features are the building blocks from which all facial images can be constructed.
Features LOCAL FEATURE ANALYSIS • Local feature analysis selects features in each face that differ most from other faces such as, the nose, eyebrows, mouth and the areas where the curvature of the bones changes.
LOCAL FEATURE ANALYSIS To determine someone's identity, • the computer takes an image of that person and • determines the pattern of points that make that individual differ most from other people. Then the system starts creating patterns, • either randomly or • based on the average Eigenface.
LOCAL FEATURE ANALYSIS (e) For each selection, the computer constructs a face image and compares it with the target face to be identified. (f) New patterns are created until (g) A facial image that matches with the target can be constructed. When a match is found, the computer looks in its database for a matching pattern of a real person (h), as shown below.
PERFORMANCE ISSUES From Eigenface Technology to Local Feature Analysis, the problems faced were same: • Images with complex backgrounds • Poor lighting conditions • Recognition accuracy.
NEURAL NETWORK TECHNOLOGY • Features from the entire face are extracted as visual contrast elements such as the eyes, side of the nose, mouth, eyebrows, cheek-line and others (Feature Extraction). • The features are quantified, normalized and compressed into a template code.
Valid user/ Invalid user? Feature Extraction ARTIFICIAL NEURAL NETWORK ANN technology gives computer systems an amazing capacity to actually learn from input data. Features provided to ANN Hidden Layer Input Layer Output Layer
Since,the neural network learns from experience, it does a better job of accommodating varying lighting conditions and improves accuracy over any other method.
ADVANTAGES DISADVANTAGES Advantages • Less intrusive • Major security boost • Fast • Simple Recognition • Disadvantages • Breach of privacy • Comparatively less accurate • Expensive to implement
BIOMETRICS FUTURE ADVANCES • BIOMETRIC SYSTEMS INTEGRATION SERVICES WHICH COMBINE FACE RECOGNITION SOFTWARE WITH OTHER BIOMETRICS, SUCH AS IRIS, VOICE, SIGNITURE, FINGERPRINT AS WELL AS EXISTING IDENTIFICATION CARD SYSTEMS • A PERSONS FACE WILL BE THE PRIVATE, SECURE AND CONVENIENT PASSWORD